NEURAL NETWORKS FOR STOCK MARKET OPTION PRICING
Sergey A. Sannikov
Introduction: The use of neural networks for non-linear models helps to understand where linear model drawbacks, coused by their specification, reveal themselves. This paper attempts to find this out. The objective of research is to determine the meaning of “option prices calculation using neural networks”.
Materials and Methods: We use two kinds of variables: endogenous (variables included in the model of neural network) and variables affecting on the model (permanent disturbance).
Results: All data are divided into 3 sets: learning, affirming and testing. All selected variables are normalised from 0 to 1. Extreme values of income were shortcut.
Discussion and Conclusions: Using the 33-14-1 neural network with direct links we obtained two sets of forecasts. Optimal criteria of strategies in stock markets’ option pricing were developed.
Keywords: MBPN model, volatility, mean square estimation, neural network, stock market option
For citation: Sannikov SA. Neural networks for stock market option pricing. Vestnik Mordovskogo universiteta = Mordovia University Bulletin. 2017; 1(27):21-26. DOI: 10.15507/0236-2910.027.201701.021-026
The author have read and approved the final manuscript.
This work is licensed under a Creative Commons Attribution 4.0 License.